Text Classification
Adapters
Safetensors
Transformers
English
gemma
geospatial
amd-accelerated
natural-language-processing
command-generation
text-generation-inference
Instructions to use prof-freakenstein/test-gemma-isro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use prof-freakenstein/test-gemma-isro with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("fill-in-model-name") model.load_adapter("prof-freakenstein/test-gemma-isro", set_active=True) - Transformers
How to use prof-freakenstein/test-gemma-isro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prof-freakenstein/test-gemma-isro")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prof-freakenstein/test-gemma-isro") model = AutoModelForSequenceClassification.from_pretrained("prof-freakenstein/test-gemma-isro", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b98c2fa30b17843db34d3ffbf727e970613a8d678c292365bbacd96fca236a09
- Size of remote file:
- 5.11 kB
- SHA256:
- 3547b0b873346d2b980318d61a8f002bb41ded4698b835a06cecc93a30cd0cfa
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